{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T19:31:24Z","timestamp":1794079884386,"version":"4.8.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-23617-4","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T15:28:10Z","timestamp":1763134090000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Real-time deforestation anomaly detection using YOLO and LangChain agents for sustainable environmental monitoring"],"prefix":"10.1038","volume":"15","author":[{"given":"Shakti","family":"Kundu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shalini Zanzote","family":"Ninoria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi Prakash","family":"Chaturvedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annu","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshat","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reenu","family":"Batra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitiku","family":"Dubale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arshad","family":"Hashmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"23617_CR1","doi-asserted-by":"publisher","unstructured":"Adjei, V. & Antwi, M. Land use change detection using the intensity analysis at the Bosomtwe District, Ashanti Region, Ghana. Smart Agricultural Technol. 5, 100290. https:\/\/doi.org\/10.1016\/j.atech.2023.100290 (October 2023).","DOI":"10.1016\/j.atech.2023.100290"},{"key":"23617_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.3039875","author":"V Akbari","year":"2022","unstructured":"Akbari, V. & Solberg, S. Clear-Cut Detection and Mapping Using Sentinel-1 Backscatter Coefficient and Short-Term Interferometric Coherence Time Series. IEEE Geosci. Remote Sens. Lett. 19 : 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2020.3039875. (2022)."},{"key":"23617_CR3","doi-asserted-by":"publisher","unstructured":"Almeida, J. et al. EdgeFireSmoke: A novel lightweight CNN model for Real-Time video Fire\u2013Smoke detection. IEEE Trans. Industr. Inf. 18, 11. https:\/\/doi.org\/10.1109\/TII.2021.3138752 (November 2022).","DOI":"10.1109\/TII.2021.3138752"},{"key":"23617_CR4","doi-asserted-by":"publisher","unstructured":"Almeida-Filho, R., Rosenqvist, A., Shimabukuro, Y. E. & dosSantos, J. R. Evaluation and perspectives of using multitemporal L-Band SAR data to monitor deforestation in the Brazilian Amaz\u00d4nia. IEEE Geosci. Remote Sens. Lett. 2 (4), 409\u2013412. https:\/\/doi.org\/10.1109\/LGRS.2005.856679 (2005).","DOI":"10.1109\/LGRS.2005.856679"},{"key":"23617_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2024.3355104","volume":"21","author":"M Alshehri","year":"2024","unstructured":"Alshehri, M., Ouadou, A., Grant, J. & Scott Deep Transformer-Based network deforestation detection in the Brazilian Amazon using Sentinel-2 imagery. IEEE Geosci. Remote Sens. Lett. 21, 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2024.3355104 (2024).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"23617_CR6","doi-asserted-by":"publisher","first-page":"10314","DOI":"10.1109\/JSTARS.2021.3110460","volume":"14","author":"M Amani","year":"2021","unstructured":"Amani, M. et al. Wetland change analysis in Alberta, Canada using four decades of Landsat imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 14, 10314\u201310335. https:\/\/doi.org\/10.1109\/JSTARS.2021.3110460 (2021).","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"23617_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3029841","author":"D Amitrano","year":"2021","unstructured":"Amitrano, D., Guida, R. & Iervolino, P. Semantic Unsupervised Change Detection of Natural Land Cover With Multitemporal Object-Based Analysis on SAR Images. IEEE Trans. Geosci. Remote Sens. 59, 7 : 5494\u20135514. https:\/\/doi.org\/10.1109\/TGRS.2020.3029841. (2021)."},{"key":"23617_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2024.3454973","volume":"21","author":"I Borlido","year":"2024","unstructured":"Borlido, I. et al. How to identify good superpixels for deforestation detection on tropical rainforests. IEEE Geosci. Remote Sens. Lett. 21, 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2024.3454973 (2024).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"23617_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2013.2272545","author":"EB Brooks","year":"2014","unstructured":"Brooks, E. B. et al. On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data. IEEE Trans. Geosci. Remote Sens. 52, 6 : 3316\u201332. https:\/\/doi.org\/10.1109\/TGRS.2013.2272545.  (2014)."},{"key":"23617_CR10","doi-asserted-by":"publisher","first-page":"8622","DOI":"10.1080\/01431161.2024.2403628","volume":"45","author":"Chatrabhuj","year":"2024","unstructured":"Chatrabhuj & Meshram, K. Incremental learning model for sustainable agricultural land assessment using multimodal satellite data. Int. J. Remote Sens. 45, 8622\u20138648. https:\/\/doi.org\/10.1080\/01431161.2024.2403628 (2024).","journal-title":"Int. J. Remote Sens."},{"issue":"3","key":"23617_CR11","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1007\/s40098-024-00907-8","volume":"54","author":"Chatrabhuj","year":"2024","unstructured":"Chatrabhuj & Meshram, K. Design of an efficient satellite image analysis model for identification of sustainable construction areas via ground subsidence monitoring and infrastructure monitoring operations. Indian Geotech. J. 54 (3), 1136\u20131151 (2024).","journal-title":"Indian Geotech. J."},{"key":"23617_CR12","doi-asserted-by":"publisher","unstructured":"Chatrabhuj & Meshram, K. Proposed design of an augmented deep learning model for Estimation of sustainable development goals via. Satell. Image Anal. Environ. Dev. Sustain. 1\u201327. https:\/\/doi.org\/10.1007\/s10668-023-04360-0 (2024).","DOI":"10.1007\/s10668-023-04360-0"},{"key":"23617_CR13","doi-asserted-by":"publisher","unstructured":"Colditz, R. R., Ricardo, M., Llamas & Ressl, R. A. Detecting change areas in Mexico between 2005 and 2010 using 250 m MODIS images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7, 8. https:\/\/doi.org\/10.1109\/JSTARS.2013.2280711 (2014).","DOI":"10.1109\/JSTARS.2013.2280711"},{"key":"23617_CR14","doi-asserted-by":"publisher","unstructured":"Da Silva, E. & Freitas Jos\u00e9 Tasso Felix Guimar\u00e3es, Gabriel Negreiros Salom\u00e3o, Prafulla Kumar Sahoo, Douglas Batista Da Silva Ferreira, Mariana Maha Jana Costa Figueiredo, Silvio Junio Ramos. Multidecadal analysis of erosion susceptibility in a watershed heavily impacted by deforestation in southeastern Amazonia. Int. J. Sediment. Res. Febr. https:\/\/doi.org\/10.1016\/j.ijsrc.2025.01.010 (2025).","DOI":"10.1016\/j.ijsrc.2025.01.010"},{"key":"23617_CR15","doi-asserted-by":"publisher","unstructured":"Dalal, S. et al. A deep learning framework with learning without forgetting for intelligent surveillance in IoT-enabled home environments in smart Cities. Recent advances in computer science and communications https:\/\/doi.org\/10.2174\/0126662558329951241024183922 (2024).","DOI":"10.2174\/0126662558329951241024183922"},{"key":"23617_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.3032098","author":"FBJ Dallaqua","year":"2022","unstructured":"Dallaqua, F. B. J., Rojas, F. A. & Faria and Alvaro Luiz Fazenda. Building Data Sets for Rainforest Deforestation Detection Through a Citizen Sci. Project IEEE Geoscience Remote Sens. Lett. 19 : 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2020.3032098. (2022)."},{"key":"23617_CR17","doi-asserted-by":"publisher","unstructured":"Das, P., Kumar, A., Sahu, D. V., Xavy & Meher, S. A deforestation detection network using deep Learning-Based semantic segmentation. IEEE Sens. Lett. 8 (1), 1\u20134. https:\/\/doi.org\/10.1109\/LSENS.2023.3340562 (2024).","DOI":"10.1109\/LSENS.2023.3340562"},{"key":"23617_CR18","doi-asserted-by":"publisher","unstructured":"Diniz, C. G., Souza, A. A. D. A. & Santos, D. C. Mirian Correa Dias, Nelton Cavalcante Da Luz, Douglas Rafael Vidal De Moraes, Janaina Sant Ana Maia. DETER-B: The New Amazon Near Real-Time Deforestation Detection System. IEEE J.  Selected Topics Appl. Earth Obs. Remote Sens. 8, no. 7July : 3619\u201328 https:\/\/doi.org\/10.1109\/JSTARS.2015.2437075. (2015).","DOI":"10.1109\/JSTARS.2015.2437075"},{"key":"23617_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3267796","author":"Z Du","year":"2023","unstructured":"Du, Z. et al. Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16 : 4195\u20134204. https:\/\/doi.org\/10.1109\/JSTARS.2023.3267796. (2023)."},{"key":"23617_CR20","doi-asserted-by":"publisher","unstructured":"Eckert, S., Schmid, L., Messerli, P. & Zaehringer, J. G. Spatiotemporal assessment of deforestation and forest degradation indicates spillover effects from mining activities and related biodiversity offsets in Madagascar. Remote Sens. Applications: Soc. Environ. 36, 101269. https:\/\/doi.org\/10.1016\/j.rsase.2024.101269 (November 2024).","DOI":"10.1016\/j.rsase.2024.101269"},{"key":"23617_CR21","doi-asserted-by":"publisher","first-page":"e2482","DOI":"10.7717\/peerj-cs.2482","volume":"10","author":"M Edeh","year":"2024","unstructured":"Edeh, M. et al. A novel deep learning model for predicting marine pollution for sustainable ocean management. PeerJ. Comput. Sci. 10, e2482 (2024).","journal-title":"PeerJ Comput. Sci."},{"key":"23617_CR22","doi-asserted-by":"publisher","unstructured":"Englhart, S., Keuck, V. & Siegert, F. Modeling aboveground biomass in tropical forests using Multi-Frequency SAR Data\u2014A comparison of methods. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 5 (1), 298\u2013306. https:\/\/doi.org\/10.1109\/JSTARS.2011.2176720 (2012).","DOI":"10.1109\/JSTARS.2011.2176720"},{"key":"23617_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3242430","volume":"20","author":"F Ferrari","year":"2023","unstructured":"Ferrari, F. & Ferreira, M. P. Cl\u00e1udio Aparecido Almeida, and Raul Queiroz Feitosa. Fusing Sentinel-1 and Sentinel-2 images for deforestation detection in the Brazilian Amazon under diverse cloud conditions. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2023.3242430 (2023).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"23617_CR24","doi-asserted-by":"publisher","unstructured":"Franke, J., Navratil, P., Keuck, V., Peterson, K. & Siegert, F. Monitoring fire and selective logging activities in tropical peat swamp forests. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 5, 1811\u20131820. https:\/\/doi.org\/10.1109\/JSTARS.2012.2202638 (2012).","DOI":"10.1109\/JSTARS.2012.2202638"},{"key":"23617_CR25","doi-asserted-by":"publisher","unstructured":"Gon\u00e7alves Sales, V. Modelling Non-Linear deforestation trends for an ecological tension zone in Brazil. Sci. Remote Sens. 7, 100076. https:\/\/doi.org\/10.1016\/j.srs.2023.100076 (023).","DOI":"10.1016\/j.srs.2023.100076"},{"key":"23617_CR26","doi-asserted-by":"publisher","unstructured":"Grings, F., Roitberg, E. & Barraza, V. EVI Time-Series breakpoint detection using convolutional networks for online deforestation monitoring in Chaco forest. IEEE Trans. Geosci. Remote Sens. 58 (2), 1303\u20131312. https:\/\/doi.org\/10.1109\/TGRS.2019.2945719 (2020).","DOI":"10.1109\/TGRS.2019.2945719"},{"key":"23617_CR27","doi-asserted-by":"publisher","unstructured":"Haq, B. et al. Tech-Driven forest conservation: combating deforestation with internet of Things, artificial Intelligence, and remote sensing. IEEE Internet Things J. 11, 14. https:\/\/doi.org\/10.1109\/JIOT.2024.3378671 (2024).","DOI":"10.1109\/JIOT.2024.3378671"},{"key":"23617_CR28","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/JSTARS.2022.3231915","volume":"16","author":"M Hermann","year":"2023","unstructured":"Hermann, M., Saha, S. & Zhu, X. X. Filtering specialized change in a Few-Shot setting. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16, 1185\u20131196. https:\/\/doi.org\/10.1109\/JSTARS.2022.3231915 (2023).","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"23617_CR29","doi-asserted-by":"publisher","unstructured":"Ji, M. et al. Deforestation detection from spaceborne Full-Waveform laser Altimetry, incorporating terrain effects: A case study in Porto Velho, Brazil. Int. J. Appl. Earth Obs. Geoinf. 129, 103861. https:\/\/doi.org\/10.1016\/j.jag.2024.103861 (2024).","DOI":"10.1016\/j.jag.2024.103861"},{"key":"23617_CR30","doi-asserted-by":"publisher","unstructured":"Kempeneers, P., Sedano, F., Strobl, P. & McInerney, D. O. Increasing robustness of postclassification change detection using time series of land cover maps. IEEE Trans. Geosci. Remote Sens. 50, 9. https:\/\/doi.org\/10.1109\/TGRS.2011.2181854 (2012).","DOI":"10.1109\/TGRS.2011.2181854"},{"key":"23617_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.872932","author":"EF Lambin","year":"2006","unstructured":"Lambin, E. F. & Linderman, M. Time Series of Remote Sensing Data for Land Change Science. IEEE Trans. Geosci. Remote Sens. 44, 7 : 1926\u201328. https:\/\/doi.org\/10.1109\/TGRS.2006.872932. (2006)."},{"key":"23617_CR32","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.gr.2024.07.014","volume":"146","author":"U Lilhore","year":"2025","unstructured":"Lilhore, U. et al. Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model. Gondwana Res. 146, 252\u2013266. https:\/\/doi.org\/10.1016\/j.gr.2024.07.014 (2025).","journal-title":"Gondwana Res."},{"key":"23617_CR33","doi-asserted-by":"publisher","unstructured":"Marcus, M. S., Hergoualc\u2019h, K. & Eur\u00eddice, N. Honorio Coronado, and V\u00edctor Hugo Guti\u00e9rrez-V\u00e9lez. Spatial distribution of degradation and deforestation of palm swamp peatlands and associated carbon emissions in the Peruvian Amazon. J. Environ. Manage. 351, 119665. https:\/\/doi.org\/10.1016\/j.jenvman.2023.119665 (2024).","DOI":"10.1016\/j.jenvman.2023.119665"},{"key":"23617_CR34","doi-asserted-by":"publisher","first-page":"13743","DOI":"10.1109\/JSTARS.2024.3429012","volume":"17","author":"S Mermoz","year":"2024","unstructured":"Mermoz, S. et al. Submonthly assessment of temperate forest Clear-Cuts in Mainland France. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 17, 13743\u201313764. https:\/\/doi.org\/10.1109\/JSTARS.2024.3429012 (2024).","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"23617_CR35","doi-asserted-by":"publisher","first-page":"101045","DOI":"10.1016\/j.envdev.2024.101045","volume":"51","author":"K Meshram","year":"2024","unstructured":"Meshram, K. Design of an iterative method for environmental-sustainable development. Integrating Bioinspired Comput. Techniques Environ. Dev. 51, 101045. https:\/\/doi.org\/10.1016\/j.envdev.2024.101045 (2024).","journal-title":"Integrating Bioinspired Comput. Techniques Environ. Dev."},{"key":"23617_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3442156","volume":"62","author":"RG Negri","year":"2024","unstructured":"Negri, R. G. et al. Unsupervised multitemporal triclass change detection. IEEE Trans. Geosci. Remote Sens. 62, 1\u201317. https:\/\/doi.org\/10.1109\/TGRS.2024.3442156 (2024).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"23617_CR37","doi-asserted-by":"publisher","unstructured":"Olding, W. C., Jan, C., Olivier, Brian, P., Salmon & Kleynhans, W. A forecasting approach to online change detection in land cover time series. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 12, 5. https:\/\/doi.org\/10.1109\/JSTARS.2019.2905594 (2019).","DOI":"10.1109\/JSTARS.2019.2905594"},{"key":"23617_CR38","doi-asserted-by":"publisher","unstructured":"Parsch, C. et al. Forecasting deforestation and carbon loss across new Guinea using machine learning and cellular automata. Sci. Total Environ. 970, 178864. https:\/\/doi.org\/10.1016\/j.scitotenv.2025.178864 (2025).","DOI":"10.1016\/j.scitotenv.2025.178864"},{"key":"23617_CR39","doi-asserted-by":"publisher","first-page":"104692","DOI":"10.1016\/j.resourpol.2024.104692","volume":"89","author":"M Radulescu","year":"2024","unstructured":"Radulescu, M., Dalal, S., Lilhore, U. K. & Sarita Saimiya. Optimizing mineral identification for sustainable resource extraction through hybrid deep learning enabled fintech model. Resour. Policy. 89, 104692. https:\/\/doi.org\/10.1016\/j.resourpol.2024.104692 (2024).","journal-title":"Resour. Policy"},{"key":"23617_CR40","doi-asserted-by":"publisher","unstructured":"Ramadan, M. N. A., Mohammed, A. H., Ali, S. Y., Khoo & Alkhedher, M. AI-Powered IoT and UAV systems for Real-Time detection and prevention of illegal logging. Results Eng. 24, 103277. https:\/\/doi.org\/10.1016\/j.rineng.2024.103277 (2024).","DOI":"10.1016\/j.rineng.2024.103277"},{"key":"23617_CR41","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2013.2245101","author":"J Reiche","year":"2013","unstructured":"Reiche, J. et al. Feature Level Fusion of Multi-Temporal ALOS PALSAR and Landsat Data for Mapping and Monitoring of Tropical Deforestation and Forest Degradation. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 6, 5 : 2159\u201373. https:\/\/doi.org\/10.1109\/JSTARS.2013.2245101.  (2013)."},{"key":"23617_CR42","doi-asserted-by":"publisher","unstructured":"Schultz, M., Verbesselt, J., Avitabile, V., Souza, C. & Herold, M. Error sources in deforestation detection using BFAST monitor on Landsat time series across three tropical sites. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 9, 8. https:\/\/doi.org\/10.1109\/JSTARS.2015.2477473 (2016).","DOI":"10.1109\/JSTARS.2015.2477473"},{"key":"23617_CR43","doi-asserted-by":"publisher","unstructured":"Soja, M. J., Henrik, J., Persson, Lars, M. H. & Ulander Modeling and detection of deforestation and forest growth in multitemporal TanDEM-X data. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 11, 10. https:\/\/doi.org\/10.1109\/JSTARS.2018.2851030 (2018).","DOI":"10.1109\/JSTARS.2018.2851030"},{"key":"23617_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2360691","author":"NT Son","year":"2015","unstructured":"Son, N. T., Chen, C. F., Chang, N. B. & Chen, C. R. Ly-Yu Chang, and Bui-Xuan Thanh. Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using Landsat Data and Object-Based Image Analysis. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 8, 2 : 503\u2013510. https:\/\/doi.org\/10.1109\/JSTARS.2014.2360691.  (2015)."},{"key":"23617_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3163575","volume":"19","author":"PJ Soto","year":"2022","unstructured":"Soto, P. J. et al. Domain-Adversarial neural networks for deforestation detection in tropical forests. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2022.3163575 (2022).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"23617_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3346378","volume":"21","author":"R Sugimoto","year":"2024","unstructured":"Sugimoto, R., Natsuaki, R., Nakamura, R., Tsutsumi, C. & Yamaguchi, Y. Time series scattering power decomposition using ensemble average in Temporal\u2013Spatial domains: application to forest disturbance detection. IEEE Geosci. Remote Sens. Lett. 21, 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2023.3346378  (2024).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"23617_CR47","doi-asserted-by":"publisher","unstructured":"Tan, K., Jin, X., Plaza, A., Wang, X., Du, P. & Liang Xiao, and Automatic change detection in High-Resolution remote sensing images by using a multiple classifier system and Spectral\u2013Spatial features. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 9, 8. https:\/\/doi.org\/10.1109\/JSTARS.2016.2541678  (2016).","DOI":"10.1109\/JSTARS.2016.2541678"},{"key":"23617_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3137277","volume":"19","author":"T Coronel","year":"2022","unstructured":"Coronel, T. Mapping deforestation using fractions indices and the Non-Seasonal PVts- \u03b2 detection approach. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2021.3137277 (2022).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"23617_CR49","doi-asserted-by":"publisher","unstructured":"Urugo, M., Makiso, M., Worku, Y. B. & Tola Ethiopian coffee: production Systems, geographical origin Traceability, and European union deforestation regulation directive compliance. J. Agric. Food Res. 19, 101695. https:\/\/doi.org\/10.1016\/j.jafr.2025.101695 (2025).","DOI":"10.1016\/j.jafr.2025.101695"},{"key":"23617_CR50","doi-asserted-by":"publisher","unstructured":"Valderrama-Landeros, L., Humberto, M. L., Baret, F. & Time Series, C. Y. C. L. O. P. E. S. L. A. I. Espana-Boquera, andDeforestation in Michoacan, Mexico, From (2000\u20132006). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 12December : 5398\u20135405. https:\/\/doi.org\/10.1109\/JSTARS.2016.2597742 (2016).","DOI":"10.1109\/JSTARS.2016.2597742"},{"key":"23617_CR51","doi-asserted-by":"publisher","first-page":"10264","DOI":"10.1109\/JSTARS.2023.3327573","volume":"16","author":"PJ Vega","year":"2023","unstructured":"Vega, P. J. & Soto Gilson Alexandre Ostwald Pedro Da Costa, Mabel Ximena Ortega Adarme, Jos\u00e9 David Berm\u00fadez Castro, and Raul Queiroz Feitosa. Weakly supervised domain adversarial neural network for deforestation detection in tropical forests. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16, 10264\u201310278. https:\/\/doi.org\/10.1109\/JSTARS.2023.3327573 (2023).","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"23617_CR52","doi-asserted-by":"publisher","first-page":"85764","DOI":"10.1109\/ACCESS.2022.3194000","volume":"10","author":"H Verma","year":"2022","unstructured":"Verma, H. et al. Development of LR-PCA based fusion approach to detect the changes in Mango fruit crop by using Landsat 8 OLI images. IEEE Access. 10, 85764\u201385776. https:\/\/doi.org\/10.1109\/ACCESS.2022.3194000 (2022).","journal-title":"IEEE Access."},{"key":"23617_CR53","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2010.2076398","author":"WS Walker","year":"2010","unstructured":"Walker, W. S., Stickler, C. M., Kellndorfer, J. M., Kirsch, K. M. & Nepstad, D. C. Large-Area Classification and Mapping of Forest and Land Cover in the Brazilian Amazon: A Comparative Analysis of ALOS\/PALSAR and Landsat Data Sources. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 3, 4 : 594\u2013604. https:\/\/doi.org\/10.1109\/JSTARS.2010.2076398. (2010)."},{"key":"23617_CR54","doi-asserted-by":"publisher","first-page":"18593","DOI":"10.1109\/JSTARS.2024.3435372","volume":"17","author":"Z Wang","year":"2024","unstructured":"Wang, Z. et al. A Web-Based prototype system for deforestation detection on High-Resolution remote sensing imagery with deep learning. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 17, 18593\u201318612. https:\/\/doi.org\/10.1109\/JSTARS.2024.3435372 (2024).","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"23617_CR55","doi-asserted-by":"publisher","unstructured":"Watanabe, M., Koyama, C. N., Hayashi, M., Nagatani, I. & Shimada, M. Early-Stage deforestation detection in the tropics with L -Band SAR. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 11, 6. https:\/\/doi.org\/10.1109\/JSTARS.2018.2810857 (2018).","DOI":"10.1109\/JSTARS.2018.2810857"},{"key":"23617_CR56","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3312812","author":"B Yang","year":"2023","unstructured":"Yang, B. et al. Sub-Annual Scale LandTrendr: Sub-Annual Scale Deforestation Detection Algorithm Using Multi-Source Time Series Data. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16 : 8563\u20138576. https:\/\/doi.org\/10.1109\/JSTARS.2023.3312812. (2023)."},{"key":"23617_CR57","doi-asserted-by":"publisher","unstructured":"Zanotta, D., Capella, L., Bruzzone, F. & Bovolo An adaptive semisupervised approach to the detection of User-Defined recurrent changes in image time series. IEEE Trans. Geosci. Remote Sens. 53, 7. https:\/\/doi.org\/10.1109\/TGRS.2014.2381645 (2015).","DOI":"10.1109\/TGRS.2014.2381645"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23617-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23617-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23617-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:06:29Z","timestamp":1763510789000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-23617-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["23617"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-23617-4","relation":{"references":[{"id-type":"doi","id":"10.1109\/LGRS.2020.3039875","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/TGRS.2020.3029841","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/TGRS.2013.2272545","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/LGRS.2020.3032098","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/JSTARS.2023.3267796","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/TGRS.2006.872932","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/JSTARS.2013.2245101","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/JSTARS.2014.2360691","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/JSTARS.2010.2076398","asserted-by":"subject"},{"id-type":"doi","id":"10.1109\/JSTARS.2023.3312812","asserted-by":"subject"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"8 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39961"}}